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One of the pivotal parts of a research that a researcher must do right is data analysis.

Data analysis is a process that is very important in every research as it is the foundation on which the researcher lays the pillars of the theories, frameworks, and concepts of the study. In fact, data analysis provides the researcher the basis for proving the research hypothesis and for establishing the validity of the entire study.

To put it succinctly, data analysis is the process by which a researcher applies one or a combination of two or more statistical techniques such as ANOVA, sampling methods, correlation analysis, regression analysis, and multivariate analysis to describe, evaluate or illustrate a given data and subsequently make inductive inferences in the conclusion which is the most important part of a research.

Benefits of Data Analysis

The choice of the best method of data analysis for your research depends on the research model you’ve adopted; that is, whether your study is qualitative or quantitative. However, whatever your research model is, the benefits of using the right data analysis technique for evaluating your research data include the following:

  • It helps you to derive your research findings from different data sources
  • It provides you the opportunity to reduce every macro problem into smaller parts
  • It provides you meaningful insight into your raw data
  • It enables you to shut out all forms of biases when making your inferences and conclusion

It is important to state that a key step that helps in the selection of the right data analysis technique for your research is your research methodology. It is usual for researchers to review several relevant literatures and identify different methodologies earlier used from which a specific method is selected for the study under consideration.

Always Minimize Statistical Error in Data Analysis

The importance of using the right statistical technique in a research cannot be overemphasized.

For example, if you’re looking to test the differences between two or more means, ANOVA and t-test statistical techniques may come to mind but in some circumstances, one or both techniques might be inappropriate to use or else, Type 1 Error may be committed in the process.

When conducting significance test – that is, the statistical test that considers the viability of the null hypothesis, Type 1 Error is the error of rejecting a true null hypothesis when it should have been not rejected.

For example, in a psychology research titled ‘’Why Smiles Generate Leniency’’ by Marianne LaFrance and Marvin A. Hecht (1995), the researchers aimed at understanding the social importance of human smiles.

The study sought to determine ‘’whether different types of smiles generate different degrees of leniency and what mediated the effect.’’ The effects of four different types of smiles – miserable, felt, false, and neutral smiles – on the research participants were empirically investigated.

To test or analyze the differences among all the four population means for the data collected for this study, a researcher may decide to use any of the most commonly used statistical techniques for evaluating differences among means, that is, ‘t test’ and ANOVA.

If a researcher decides to use the t-test for this study, six comparisons of mean groups – false vs. felt, false vs. miserable, false vs. neutral, felt vs. miserable, felt vs. neutral, and miserable vs neutral -will be generated and the analysis will lead to the probability of making a Type 1 Error.

Also, when pairs of means are compared, the complexity is obvious especially when the group of means to be compared exceeded two.

For example, if there are 12 means, 66 possible comparisons – as shown in Figure 1 – will be generated which is rather complex to deal with thereby leading to avoidable errors in data analysis.

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Figure 1: The number of means determine possible comparisons of means

In addition, the probability that Type 1 Error will be made is greater because it depends on the number of means being compared as shown in Figure 2 below:

data analysis

Figure 2: The probability of making Type 1 Error depends on the number of means being compared

However, if Tukey Honesty Significant Difference (Tukey HSD) test is used, the probability of making Type 1 Error can be greatly reduced. The Tukey HSD test is a variation of the t-distribution and it takes into consideration the number of means being compared.

Always Use a Technique that Offers More Specific Results

Again, if ANOVA test statistic is used to analyze the data collected from the research: ‘’Why Smiles Generate Leniency,’’ then ANOVA will test the null hypothesis that suggests that all four group means derived from the data collected are equal thereby creating another problem for the researcher or where the researcher is unaware of the problem, it will create a gap in the entire research findings.

This is because the null hypothesis – that is: μfalse = μfelt = μmiserable = μneutral – in this research is non-specific and if it is rejected, it means that at least one group mean out of the four-group means is different from at least one other group mean and ANOVA won’t reveal which of the four means is different.

What that means is that the choice of data analysis provides less specific results when compared to other statistical tests such as the Tukey HSD test. Notwithstanding this seeming defect of ANOVA, it is still relevant as it has some specific areas of application where its use is the best in the circumstance.

Indeed, the benefits of a professionally done data analysis are great for the researcher. It can bring to your research some research findings that would not have otherwise been possible as outlined above.

There is no doubt that every researcher is qualified. In fact, these people are highly knowledgeable in their fields which are why they are working on specific areas of research that appeals to them as master’s degree candidates or doctoral degree candidates but often, dear researcher, the services of an experienced statistical consultant are needed to complement your specialized skills.

A statistical consultant is a specialist in statistics whose skills and experiences can help you solve a range of research-related problems by analyzing your research data and answering some specific questions.

Once contacted, a statistical consultant will liaise with you to define the scope of the statistical needs in your research. In some cases, the consultant will only provide the needed statistical advice that will be sufficient for you to carry out the analysis of your data and if it is necessary for them to analyze and interpret the data for you, it is their job and they will work within the scope of their engagement.

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